Optical neural network identifies objects at speed of light

Unlike typical machine vision systems that use cameras and imaging software, the new device does not convert light into data for processing. Called a "diffractive deep neural network," it uses the light bouncing from the object itself to identify it in the same time it would take a computer to simply “see” the object. Essentially operating at the speed of light, it has many potential applications in areas such as instantaneous vision systems for autonomous vehicles and medical diagnostics. The research is published in Science.  

"This work opens up fundamentally new opportunities to use an artificial intelligence-based passive device to instantaneously analyse data, images and classify objects," said Aydogan Ozcan, the study's principal investigator and the UCLA Chancellor's Professor of Electrical and Computer Engineering. "This optical artificial neural network device is intuitively modelled on how the brain processes information. It could be scaled up to enable new camera designs and unique optical components that work passively in medical technologies, robotics, security or any application where image and video data are essential."